Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges
Towards future intelligent vehicular network, the machine learning as the promising artificial
intelligence tool is widely researched to intelligentize communication and networking …
intelligence tool is widely researched to intelligentize communication and networking …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
MEC-assisted immersive VR video streaming over terahertz wireless networks: A deep reinforcement learning approach
J Du, FR Yu, G Lu, J Wang, J Jiang… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced
immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user …
immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user …
A survey of incentive mechanism design for federated learning
Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …
clients without exposing their raw data. It can not only enable the clients to preserve the …
Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems
Mobile Edge Computing (MEC) is one of the most promising techniques for next-generation
wireless communication systems. In this paper, we study the problem of dynamic caching …
wireless communication systems. In this paper, we study the problem of dynamic caching …
When machine learning meets privacy in 6G: A survey
The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
Multi-agent deep reinforcement learning for computation offloading and interference coordination in small cell networks
X Huang, S Leng, S Maharjan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Integrating mobile edge computing (MEC) with small cell networks has been conceived as a
promising solution to provide pervasive computing services. However, the interactions …
promising solution to provide pervasive computing services. However, the interactions …
Latency minimization for D2D-enabled partial computation offloading in mobile edge computing
We consider Device-to-Device (D2D)-enabled mobile edge computing offloading scenario,
where a device can partially offload its computation task to the edge server or exploit the …
where a device can partially offload its computation task to the edge server or exploit the …
Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond
5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …